{"id":"W1186966557","doi":"10.1016/j.petlm.2015.07.008","title":"Evolving simple-to-use method to determine water–oil relative permeability in petroleum reservoirs","year":2015,"lang":"en","type":"article","venue":"Petroleum","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Particle swarm optimization; Relative permeability; Artificial neural network; Genetic algorithm; Relative standard deviation; Permeability (electromagnetism); Computer science; Mathematical optimization; Biological system; Artificial intelligence; Engineering; Algorithm; Machine learning; Mathematics; Chemistry; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001537803,0.0004127048,0.0005827966,0.0005635674,0.00005203093,0.0001101762,0.0004878411,0.000193389,0.00006952296],"category_scores_gemma":[0.001134348,0.0003825058,0.0001222245,0.000400353,0.00002923933,0.000870542,0.0002427091,0.0005510753,0.0002370356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001198139,"about_ca_system_score_gemma":0.00004173941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004571378,"about_ca_topic_score_gemma":0.001104192,"domain_scores_codex":[0.9971469,0.0002469344,0.0006132277,0.0006198545,0.0004554797,0.0009176287],"domain_scores_gemma":[0.9980762,0.0003817472,0.00003873997,0.0008292991,0.0001627113,0.0005113187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004674374,0.0002121802,0.05055016,0.0003448151,0.0001305321,0.0001537308,0.008153266,0.6074794,0.3018285,0.0001740728,0.007420493,0.0230854],"study_design_scores_gemma":[0.002453659,0.001296451,0.03788886,0.0005692598,0.00007679286,0.00005237772,0.0008000549,0.3127104,0.513231,0.00790392,0.1200632,0.002954038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8409678,0.00006197953,0.1447371,0.0003488372,0.0002654605,0.0001839242,0.00003466722,0.001022737,0.01237747],"genre_scores_gemma":[0.8920733,0.000009439487,0.1058252,0.0001399819,0.00008436281,0.0002406291,0.00001756388,0.0001158149,0.001493724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.294769,"threshold_uncertainty_score":0.9998627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03082138777568824,"score_gpt":0.2871754389139938,"score_spread":0.2563540511383056,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}